class_docstr: Model trainer
  
  Args:
      model(paddle.nn.Layer) : Model to train or evaluate.
      optimizer(paddle.optimizer.Optimizer) : Optimizer for loss.
      use_gpu(bool) : Whether to use gpu to run.
      use_vdl(bool) : Whether to use visualdl to record training data.
      checkpoint_dir(str) : Directory where the checkpoint is saved, and the trainer will restore the
          state and model parameters from the checkpoint.
      compare_metrics(callable) : The method of comparing the model metrics. If not specified, the main
          metric return by `validation_step` will be used for comparison by default, the larger the
          value, the better the effect. This method will affect the saving of the best model. If the
          default behavior does not meet your requirements, please pass in a custom method.
  
          Example:
              .. code-block:: python
  
                  def compare_metrics(old_metric: dict, new_metric: dict):
                      mainkey = list(new_metric.keys())[0]
                      return old_metric[mainkey] < new_metric[mainkey]
docstr: Train a model with specific config.
  
  Args:
      train_dataset(paddle.io.Dataset) : Dataset to train the model
      epochs(int) : Number of training loops, default is 1.
      batch_size(int) : Batch size of per step, default is 1.
      num_workers(int) : Number of subprocess to load data, default is 0.
      eval_dataset(paddle.io.Dataset) : The validation dataset, deafult is None. If set, the Trainer will
          execute evaluate function every `save_interval` epochs.
      log_interval(int) : Log the train infomation every `log_interval` steps.
      save_interval(int) : Save the checkpoint every `save_interval` epochs.
      collate_fn(callable): function to generate mini-batch data by merging the sample list.
          None for only stack each fields of sample in axis 0(same as :attr::`np.stack(..., axis=0)`). Default None
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code: def train(
      self,
      train_dataset: paddle.io.Dataset,
      epochs: int = 1,
      batch_size: int = 1,
      num_workers: int = 0,
      eval_dataset: paddle.io.Dataset = None,
      log_interval: int = 10,
      save_interval: int = 10,
      collate_fn: Callable = None,
  ):
      """
      Train a model with specific config.
  
      Args:
          train_dataset(paddle.io.Dataset) : Dataset to train the model
          epochs(int) : Number of training loops, default is 1.
          batch_size(int) : Batch size of per step, default is 1.
          num_workers(int) : Number of subprocess to load data, default is 0.
          eval_dataset(paddle.io.Dataset) : The validation dataset, deafult is None. If set, the Trainer will
              execute evaluate function every `save_interval` epochs.
          log_interval(int) : Log the train infomation every `log_interval` steps.
          save_interval(int) : Save the checkpoint every `save_interval` epochs.
          collate_fn(callable): function to generate mini-batch data by merging the sample list.
              None for only stack each fields of sample in axis 0(same as :attr::`np.stack(..., axis=0)`). Default None
      """
      if eval_dataset is not None:
          if isinstance(self.model, paddle.DataParallel):
              model = self.model._layers
          else:
              model = self.model
  
          if not hasattr(model, "validation_step"):
              raise NotImplementedError(
                  "The specified finetuning model does not support evaluation."
              )
  
      batch_sampler = paddle.io.DistributedBatchSampler(
          train_dataset,
          batch_size=batch_size,
          shuffle=True,
          drop_last=False,
      )
      loader = paddle.io.DataLoader(
          train_dataset,
          batch_sampler=batch_sampler,
          num_workers=num_workers,
          return_list=True,
          use_buffer_reader=True,
          collate_fn=collate_fn,
      )
  
      steps_per_epoch = len(batch_sampler)
      timer = Timer(steps_per_epoch * epochs)
      timer.start()
  
      for i in range(epochs):
          self.current_epoch += 1
          avg_loss = 0
          avg_metrics = defaultdict(int)
          self.model.train()
  
          for batch_idx, batch in enumerate(loader):
              loss, metrics = self.training_step(batch, batch_idx)
              self.optimizer_step(
                  self.current_epoch, batch_idx, self.optimizer, loss
              )
              self.optimizer_zero_grad(
                  self.current_epoch, batch_idx, self.optimizer
              )
  
              # calculate metrics and loss
              avg_loss += float(loss)
              for metric, value in metrics.items():
                  if isinstance(value, paddle.Tensor):
                      value = value.numpy()
                  avg_metrics[metric] += value
  
              timer.count()
  
              if (
                  batch_idx + 1
              ) % log_interval == 0 and self.local_rank == 0:
                  lr = self.optimizer.get_lr()
                  avg_loss /= log_interval
                  if self.use_vdl:
                      self.log_writer.add_scalar(
                          tag="TRAIN/loss",
                          step=timer.current_step,
                          value=avg_loss,
                      )
  
                  print_msg = "Epoch={}/{}, Step={}/{}".format(
                      self.current_epoch,
                      epochs,
                      batch_idx + 1,
                      steps_per_epoch,
                  )
                  print_msg += " loss={:.4f}".format(avg_loss)
  
                  for metric, value in avg_metrics.items():
                      value /= log_interval
                      if self.use_vdl:
                          self.log_writer.add_scalar(
                              tag="TRAIN/{}".format(metric),
                              step=timer.current_step,
                              value=value,
                          )
                      if isinstance(value, np.ndarray):
                          value = value.item()
                      print_msg += " {}={:.4f}".format(metric, value)
  
                  print_msg += (
                      " lr={:.6f} step/sec={:.2f} | ETA {}".format(
                          lr, timer.timing, timer.eta
                      )
                  )
  
                  logger.train(print_msg)
  
                  avg_loss = 0
                  avg_metrics = defaultdict(int)
  
              if (
                  self.current_epoch % save_interval == 0
                  and batch_idx + 1 == steps_per_epoch
                  and self.local_rank == 0
              ):
                  if eval_dataset:
                      result = self.evaluate(
                          eval_dataset,
                          batch_size,
                          num_workers,
                          collate_fn=collate_fn,
                      )
                      eval_loss = result.get("loss", None)
                      eval_metrics = result.get("metrics", {})
                      if self.use_vdl:
                          if eval_loss:
                              self.log_writer.add_scalar(
                                  tag="EVAL/loss",
                                  step=timer.current_step,
                                  value=eval_loss,
                              )
  
                          for metric, value in eval_metrics.items():
                              self.log_writer.add_scalar(
                                  tag="EVAL/{}".format(metric),
                                  step=timer.current_step,
                                  value=value,
                              )
  
                      if not self.best_metrics or self.compare_metrics(
                          self.best_metrics, eval_metrics
                      ):
                          self.best_metrics = eval_metrics
                          best_model_path = os.path.join(
                              self.checkpoint_dir, "best_model"
                          )
                          self.save_model(best_model_path)
                          self._save_metrics()
  
                          metric_msg = [
                              "{}={:.4f}".format(metric, value)
                              for metric, value in self.best_metrics.items()
                          ]
                          metric_msg = " ".join(metric_msg)
                          logger.eval(
                              "Saving best model to {} [best {}]".format(
                                  best_model_path, metric_msg
                              )
                          )
  
                  self._save_checkpoint()
  
imports: import os
  import pickle
  import time
  from collections import defaultdict
  from typing import Any
  from typing import Callable
  from typing import Generic
  from typing import List
  import numpy
  import paddle
  from visualdl import LogWriter
  from paddlehub.utils.log import logger
  from paddlehub.utils.utils import Timer
label_desc: Use image node_id 8 for calling the Trainer obj's underlying member method code with example usage: obj.train(train_dataset, epochs, batch_size, num_workers, eval_dataset, log_interval, save_interval, collate_fn) without return types